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Cross-sentence N-ary Relation Extraction using Entity Link and Discourse Relation

Authors:

Yeonsoo LeeAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 705 - 714

https://doi.org/10.1145/3340531.3412011

Published: 19 October 2020 Publication History

Abstract

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.

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Cited By

Chen JLi ZYu HZhang X(2024)A Weighted Diffusion Graph Convolutional Network for Relation ExtractionJournal of Electrical and Computer Engineering10.1155/2024/87296212024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/8729621
Zhao XDeng YYang MWang LZhang RCheng HLam WShen YXu R(2024)A Comprehensive Survey on Relation Extraction: Recent Advances and New FrontiersACM Computing Surveys10.1145/367450156:11(1-39)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3674501
Wang QMao ZGao JZhang Y(2024)Document-level Relation Extraction with Progressive Self-distillationACM Transactions on Information Systems10.1145/365616842:6(1-34)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3656168
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Index Terms

Cross-sentence N-ary Relation Extraction using Entity Link and Discourse Relation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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October 19 - 23, 2020

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Cited By

Chen JLi ZYu HZhang X(2024)A Weighted Diffusion Graph Convolutional Network for Relation ExtractionJournal of Electrical and Computer Engineering10.1155/2024/87296212024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/8729621
Zhao XDeng YYang MWang LZhang RCheng HLam WShen YXu R(2024)A Comprehensive Survey on Relation Extraction: Recent Advances and New FrontiersACM Computing Surveys10.1145/367450156:11(1-39)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3674501
Wang QMao ZGao JZhang Y(2024)Document-level Relation Extraction with Progressive Self-distillationACM Transactions on Information Systems10.1145/365616842:6(1-34)Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3656168
Ge JCao JBao YCao BLiu B(2024)GAL: combining global and local contexts for interpersonal relation extraction toward document-level Chinese textNeural Computing and Applications10.1007/s00521-023-09336-936:11(5715-5731)Online publication date: 12-Jan-2024
https://doi.org/10.1007/s00521-023-09336-9
Zhang DLiu ZJia WWu FLiu HTan J(2023)Dual Attention Graph Convolutional Network for Relation ExtractionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3289879(1-14)Online publication date: 2023
https://doi.org/10.1109/TKDE.2023.3289879
Li RYang CLi TSu S(2022)MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation ExtractionACM Transactions on Information Systems10.1145/350391740:4(1-32)Online publication date: 11-Jan-2022
https://dl.acm.org/doi/10.1145/3503917
Zhang MQian TLiu B(2022)Exploit Feature and Relation Hierarchy for Relation ExtractionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2022.315325630(917-930)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1109/TASLP.2022.3153256
Chen XZhang MXiong SQian T(2022)On the form of parsed sentences for relation extractionKnowledge-Based Systems10.1016/j.knosys.2022.109184251:COnline publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109184

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